Mapping Cyclist Safety in New York City

Is there a relationship between bike path access and cyclist injuries?

I was originally inspired by this CityLab article detailing the then-impending transportation crisis due to the L Line shutdown. Specifically, I was interested in observing whether the shutdown contributed to an increase in cyclist injuries due to motor vehicle collisions as about 275,000 L Line riders would have sought alternatives. The MTA averted the shutdown at the 11th hour, leading me to a new research question.

NYC has seen an exponential increase in bicycle usage in the last 20 years. It reports up to 76% fewer injuries or deaths per 10 million miles biked since 2000, a measure of bike safety that controls for increased usage. There is concurrent evidence that access to public transit is disparate throughout the city. I was unable to find reliable sources on equitable bike lane access.

I hypothesize there are neighborhoods with outsized shares of cyclist injuries which can be predicted by lack of access to (protected) bike lanes, lack of access to public transit, and faster street traffic. I hope to inform a discussion around resource allocation to low-income neighborhoods where preventable injury and death hamper cyclist transportation.

Let’s get a quick understanding of bikes in New York.

When do cyclist injuries and accidents happen?

What kinds of bike paths are available in the city?

Where do cyclist injuries and deaths occur?

An alternative map implemented in Deck.gl:

Recreating the DOT’s cyclist safety metric across neighborhoods proves difficult with existing data. Their metric is available as-is and tracks injuries and deaths per 10 million miles over time, but there is no source that tracks miles biked in any strict location-based dimension.

I use NTA population as a proxy for bike ridership with the understanding that neighborhood population doesn’t directly correlate with ridership. This is done to control for dense neighborhoods that may have higher incidence of accidents due to more people traveling through them.

Note: After further literature review, an alternative approach would be to model accident frequency for each street segment, including bike path occurance as a feature of each street segment (among other features). This allows for a more robust understanding of the interaction of bike paths with accident frequency. I could then go on to include socioeconomic data as a feature in the model or otherwise aggregate streets to neighborhoods to answer my primary research question. I prefer this approach to what I currently have implemented.

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Planned Updates / Notes:

This is an ongoing project that I am doing in my free time. If you have any constructive criticism please feel free to reach out, especially with suggestions about spatial regression methodology.

• Methodology
  • Need a way to do geospatial predictive modeling with a dataset that only has 1s, no 0s
  • How to understand relationship between accidents when we only have accident data, not non-accidents
  • Consider using Citibike data as a proxy to cyclist population, but do they behave differently?
  • Look into spatial prediction methods (spatial autocorrelation, spatial regression, etc.)
  • What trends are there in types of bike paths being constructed over time?
• Time-Series
  • More rigorous analysis of cyclist injuries
  • Use ARIMA model to remove seasonality, conclude whether injuries are increasing
  • Are injuries increasing because more people are biking or something else?
    
• Geospatial
  • Consider changing map to chloropleths (and use accidents per capita)
  • Find a way to visualize changes over time, whether through leaflet or gganimate or otherwise
  • Need function that can calculate a point’s (accident) nearest polyline (bike path) and return features (type of path)